With increasing popularity of automation and intelligent electronic devices, such as computerized machines, IoT (the Internet of Things), smart vehicles, smart phones, drones, mobile devices, airplanes, artificial intelligence (“AI”), the demand of intelligent machines and faster real-time response are increasing. To properly provide machine learning, a significant number of pieces, such as data management, model training, and data collection, needs to be improved.
A conventional type of machine learning is, in itself, an exploratory process which may involve trying different kinds of models, such as convolutional, RNN (recurrent neural network), et cetera. Machine learning or training typically concerns a wide variety of hyper-parameters that change the shape of the model and training characteristics. Model training generally requires intensive computation. As such, real-time response via machine learning model can be challenging.
A drawback associated to a conventional navigation system is that it provides turn by turn and specific lane instructions without sufficiently high resolution to determine which lane the car is currently in. Another drawback with a typical GPS (global positioning system) navigational system is that the raw GPS usually has wide variance and errors, particularly in urban areas. Another problem with current navigation systems is that their goal is to give the driver directions to the specified address without any information about where to park the vehicle. For example, it is frequently the case that the goal is not to get the car to the exact address because a more practical goal is to park the car as near the address as possible and then have the driver can walk to the specified address.